Learn Machine Learning with Python using neural networks with this machine learning beginners course. In this tutorial we will look at taking an existing sol...

Learn how to build a machine learning algorithm that will use an ensemble of other algorithms to determine the best score

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Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial: How to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!

In this video we will implement a simple neural network with single neuron from scratch in python. This is also an implementation of a logistic regression in python from scratch. You know that logistic regression can be thought of as a simple neural network. The pre requisite for this tutorial is the previous tutorial on gradient descent (link below). We will be using gradient descent python funciton written in previous video to implement our own custom neural network class.

Gradient descent is the heart of all supervised learning models. It is important to understand this technique if you are pursuing a career as a data scientist or a machine learning engineer. In this video we will see a very simple explanation of what a gradient descent is for a neural network or a logistic regression (remember logistic regression is a very simple single neuron neural network). We will than implement gradient descent from scratch in python. In my machine learning tutorial series I already have a video on gradient descent but that one is on linear regression whereas this video is for logistic regression for neural network. Here is the link of my linear regression GD video,

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python

Very simple explanation of a neural network using an analogy that even a high school student can understand it easily. what is a neural network exactly? I will discuss using a simple example various concepts such as what is neuron, error backpropogation algorithm, forward pass, backward pass, neural network training etc.